Model predictive control (MPC) techniques use one or more models to predict the future behavior of an industrial process. Control signals for adjusting the industrial process are then generated based on the predicted behavior. MPC techniques have become widely accepted in various industries, such as the oil and gas, pulp and paper, food processing, and chemical industries.
When tuning an MPC or other model-based process controller for industrial use, it is often necessary or desirable to find tuning parameters that ensure good controller performance in spite of both (i) process disturbances and (ii) mismatches between a model used by the controller and the actual process. This problem falls into the discipline of “control theory” and the practice known as “robust control.” Standard robust control techniques use a concept known as “unstructured uncertainty,” which generally involves analyzing and specifying performance in the frequency domain. These robust control techniques have been used to successfully tune process controllers in a variety of industries.